{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[0.7062, 0.8367, 0.0350],\n",
      "         [0.2204, 0.6687, 0.5234],\n",
      "         [0.5149, 0.5544, 0.8493]]])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jing/anaconda3/envs/boundary/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "a = torch.rand((1, 3, 3))\n",
    "\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[ 0.0589, -0.0451,  0.0146],\n",
      "         [ 0.0260,  0.0360,  0.0116],\n",
      "         [ 0.0784, -0.0114,  0.0782]],\n",
      "\n",
      "        [[ 0.0761, -0.0081, -0.0771],\n",
      "         [ 0.0564,  0.0352, -0.0259],\n",
      "         [-0.0193, -0.0934,  0.0851]],\n",
      "\n",
      "        [[-0.0312,  0.0353,  0.0697],\n",
      "         [-0.0294, -0.0668,  0.0093],\n",
      "         [-0.0168,  0.0586, -0.0646]]])\n"
     ]
    }
   ],
   "source": [
    "b = -0.1 + 0.2 * torch.rand((3, 3, 3))\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[0.4199, 0.8045, 0.4693],\n",
      "         [0.4239, 1.0112, 0.2360],\n",
      "         [0.2178, 0.8209, 1.0368]],\n",
      "\n",
      "        [[0.5283, 0.8432, 0.5358],\n",
      "         [0.4689, 0.9220, 0.1515],\n",
      "         [0.0875, 0.7743, 1.0410]],\n",
      "\n",
      "        [[0.5709, 0.8849, 0.4792],\n",
      "         [0.4509, 0.8321, 0.2443],\n",
      "         [0.2418, 0.8754, 1.0235]]])\n"
     ]
    }
   ],
   "source": [
    "print(a + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[0.7062, 0.8367, 0.0350],\n",
      "         [0.2204, 0.6687, 0.5234],\n",
      "         [0.5149, 0.5544, 0.8493]]])\n",
      "torch.Size([1, 3, 3])\n"
     ]
    }
   ],
   "source": [
    "print(a)\n",
    "print(a.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.2204, 0.6687, 0.5234]])\n"
     ]
    }
   ],
   "source": [
    "fitness = a[:, 1]\n",
    "print(fitness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[[7.8371e-01, 1.8072e-01, 6.3328e-01, 9.3426e-01, 6.2227e-01],\n",
      "          [4.6225e-01, 1.7992e-01, 4.8865e-01, 7.1923e-01, 1.3621e-01],\n",
      "          [1.3490e-01, 7.8054e-01, 3.9491e-01, 5.9323e-01, 3.5742e-01],\n",
      "          [3.1517e-01, 8.7140e-01, 2.5628e-01, 3.3746e-01, 8.2898e-01],\n",
      "          [5.1303e-01, 4.8121e-02, 6.4878e-01, 1.9299e-01, 9.9124e-02]],\n",
      "\n",
      "         [[3.0163e-01, 2.8003e-01, 2.5982e-01, 6.4121e-01, 5.6353e-01],\n",
      "          [4.6238e-01, 7.5853e-01, 7.8596e-01, 9.7352e-01, 5.8028e-01],\n",
      "          [7.5710e-01, 6.0135e-01, 2.3046e-01, 3.5012e-01, 5.4003e-01],\n",
      "          [8.1079e-01, 8.8815e-01, 6.0630e-01, 4.2549e-01, 5.8378e-01],\n",
      "          [3.1032e-02, 6.1522e-01, 9.4986e-02, 9.0990e-01, 6.8516e-03]],\n",
      "\n",
      "         [[3.0776e-01, 5.9528e-01, 3.9982e-01, 9.8360e-01, 9.7107e-01],\n",
      "          [4.7195e-01, 6.3042e-01, 7.1076e-01, 1.1296e-01, 6.3311e-01],\n",
      "          [4.6436e-01, 4.1891e-01, 9.3852e-01, 4.3592e-01, 9.7493e-02],\n",
      "          [3.8554e-01, 3.5661e-01, 4.2667e-01, 5.8533e-01, 8.7770e-01],\n",
      "          [6.1787e-01, 2.6902e-01, 7.9455e-01, 8.8695e-01, 6.0594e-02]]],\n",
      "\n",
      "\n",
      "        [[[3.5410e-01, 8.1240e-01, 3.5306e-01, 2.2250e-01, 6.6480e-01],\n",
      "          [2.7563e-01, 4.7065e-01, 3.0153e-01, 1.8542e-01, 3.1355e-01],\n",
      "          [5.8634e-01, 9.3387e-01, 7.8591e-03, 2.5416e-01, 1.4300e-01],\n",
      "          [7.3683e-01, 8.2270e-01, 2.2632e-01, 8.2101e-01, 3.2490e-01],\n",
      "          [3.3649e-01, 7.6027e-01, 2.3265e-01, 3.1876e-01, 5.3813e-01]],\n",
      "\n",
      "         [[4.6353e-01, 4.9924e-01, 5.6508e-01, 1.5040e-01, 7.9667e-01],\n",
      "          [5.4653e-01, 6.0272e-01, 3.8455e-01, 7.4511e-02, 5.5288e-01],\n",
      "          [9.3568e-01, 1.0824e-01, 2.1945e-02, 8.2001e-01, 6.9995e-01],\n",
      "          [2.1391e-01, 1.9825e-01, 2.6740e-01, 1.4690e-01, 9.5091e-02],\n",
      "          [1.5954e-01, 4.2255e-01, 6.0424e-01, 7.2490e-01, 1.9335e-01]],\n",
      "\n",
      "         [[7.4026e-02, 6.9973e-01, 4.5324e-01, 9.3159e-01, 4.8393e-01],\n",
      "          [4.4014e-01, 3.1624e-01, 3.0456e-01, 7.0971e-01, 8.1542e-01],\n",
      "          [6.6129e-01, 8.7075e-01, 1.6924e-01, 3.0224e-01, 7.3438e-01],\n",
      "          [2.4796e-01, 2.7655e-01, 3.9640e-01, 2.6843e-01, 4.0501e-01],\n",
      "          [9.0612e-01, 8.3889e-01, 1.9363e-01, 6.7487e-01, 5.5391e-01]]],\n",
      "\n",
      "\n",
      "        [[[2.5763e-01, 1.1816e-01, 7.9204e-01, 5.3613e-01, 3.9617e-01],\n",
      "          [7.8698e-01, 4.5870e-01, 6.7918e-01, 4.6995e-01, 6.2340e-03],\n",
      "          [3.5673e-01, 9.9015e-01, 7.5981e-01, 5.7795e-01, 4.4844e-03],\n",
      "          [1.1912e-01, 6.3209e-01, 1.4834e-01, 7.4128e-01, 6.9952e-01],\n",
      "          [8.3498e-03, 5.1457e-01, 5.7242e-01, 6.3145e-01, 6.7923e-01]],\n",
      "\n",
      "         [[2.3134e-01, 5.3505e-01, 2.4863e-01, 8.1298e-01, 9.6620e-01],\n",
      "          [9.0148e-01, 8.9455e-01, 5.7209e-01, 5.2577e-01, 9.0801e-01],\n",
      "          [2.4928e-01, 6.3691e-01, 5.3751e-01, 2.8613e-01, 5.7245e-01],\n",
      "          [6.3910e-01, 1.7450e-01, 8.3492e-01, 8.5726e-01, 9.7037e-01],\n",
      "          [2.9502e-01, 1.5656e-01, 1.1433e-01, 5.4973e-02, 9.4144e-01]],\n",
      "\n",
      "         [[1.1424e-01, 3.7464e-01, 6.8069e-01, 9.0384e-01, 4.6096e-01],\n",
      "          [4.1431e-01, 8.5184e-01, 9.2672e-02, 8.0974e-01, 3.6072e-01],\n",
      "          [4.8108e-01, 1.2537e-01, 8.5311e-01, 5.1322e-01, 9.1694e-01],\n",
      "          [5.3232e-01, 6.2215e-01, 8.5476e-01, 3.0528e-01, 4.3629e-01],\n",
      "          [9.7405e-01, 5.1549e-01, 8.4944e-01, 4.4477e-01, 9.5369e-01]]],\n",
      "\n",
      "\n",
      "        [[[5.9583e-01, 1.8701e-01, 5.8528e-01, 7.3838e-01, 7.7252e-02],\n",
      "          [2.5691e-01, 2.3504e-01, 8.7550e-01, 2.5236e-01, 4.4915e-02],\n",
      "          [2.1849e-01, 9.1299e-01, 3.3200e-01, 3.0372e-01, 9.6609e-02],\n",
      "          [3.3838e-01, 3.2154e-01, 4.8592e-02, 6.1597e-01, 5.4583e-02],\n",
      "          [7.4318e-01, 2.5791e-01, 2.0856e-01, 1.2219e-02, 1.0406e-01]],\n",
      "\n",
      "         [[8.6417e-01, 6.3516e-02, 5.8481e-01, 8.0348e-01, 4.4420e-01],\n",
      "          [7.4416e-01, 3.8579e-01, 5.9599e-01, 2.9706e-01, 2.5525e-02],\n",
      "          [9.9971e-01, 3.5902e-01, 5.7195e-01, 4.3819e-01, 8.4079e-01],\n",
      "          [8.4104e-01, 9.0974e-01, 6.8148e-01, 2.9241e-01, 9.2633e-01],\n",
      "          [1.2847e-01, 3.0577e-01, 7.1349e-01, 2.1162e-01, 3.7183e-01]],\n",
      "\n",
      "         [[8.4271e-01, 3.1377e-01, 1.8776e-01, 3.0472e-01, 6.0545e-01],\n",
      "          [5.1074e-01, 1.7018e-01, 7.7840e-01, 6.1660e-01, 7.0415e-01],\n",
      "          [2.5896e-01, 4.3042e-01, 6.0924e-01, 3.5430e-01, 5.0004e-01],\n",
      "          [3.9689e-01, 5.7141e-02, 9.9804e-01, 7.5103e-01, 8.8657e-01],\n",
      "          [6.8434e-01, 2.3438e-01, 4.1604e-01, 4.8198e-01, 9.3494e-01]]],\n",
      "\n",
      "\n",
      "        [[[4.1589e-01, 4.9308e-02, 4.5784e-02, 3.1787e-01, 7.5016e-01],\n",
      "          [3.0431e-02, 1.8805e-01, 6.1713e-01, 6.9378e-01, 4.1784e-01],\n",
      "          [4.6068e-01, 5.1033e-01, 2.2053e-01, 9.7207e-01, 3.9258e-01],\n",
      "          [8.4404e-01, 6.9421e-01, 3.6588e-01, 4.0553e-02, 1.1753e-01],\n",
      "          [4.2002e-01, 7.3958e-01, 3.2869e-01, 2.7540e-01, 1.3847e-01]],\n",
      "\n",
      "         [[4.2622e-01, 5.4415e-01, 3.7395e-01, 8.3139e-02, 6.2065e-01],\n",
      "          [5.0713e-01, 4.1610e-04, 4.3449e-01, 3.4294e-01, 1.4015e-01],\n",
      "          [8.0542e-01, 9.9325e-01, 3.3394e-02, 3.5330e-01, 1.1856e-01],\n",
      "          [2.2243e-01, 7.1571e-01, 5.0505e-01, 9.2740e-01, 3.5364e-01],\n",
      "          [1.7053e-01, 5.3778e-01, 4.4430e-01, 4.1054e-03, 6.2149e-01]],\n",
      "\n",
      "         [[2.2738e-01, 7.6363e-01, 9.8940e-01, 8.5282e-01, 9.5875e-01],\n",
      "          [3.3750e-01, 8.7118e-02, 3.7848e-01, 7.0478e-01, 8.5666e-01],\n",
      "          [1.3563e-01, 6.6197e-01, 2.9106e-01, 5.3647e-01, 6.5325e-01],\n",
      "          [4.3062e-01, 9.0902e-01, 5.6690e-01, 1.3312e-01, 1.6582e-01],\n",
      "          [8.3589e-01, 1.5021e-01, 7.4287e-01, 7.2208e-01, 3.2755e-01]]],\n",
      "\n",
      "\n",
      "        [[[5.0141e-01, 2.3339e-01, 9.2004e-01, 5.8483e-01, 8.2268e-01],\n",
      "          [5.4677e-01, 1.0470e-01, 1.0831e-01, 4.0758e-01, 3.5503e-01],\n",
      "          [4.1090e-01, 7.8266e-01, 3.4415e-01, 3.8203e-01, 9.6896e-01],\n",
      "          [3.5812e-01, 7.2064e-01, 3.6556e-01, 6.2052e-01, 4.8416e-01],\n",
      "          [7.5028e-02, 7.5607e-01, 6.4777e-01, 7.7449e-01, 6.2868e-01]],\n",
      "\n",
      "         [[7.1022e-01, 7.9688e-01, 8.2336e-01, 8.4440e-01, 8.5674e-01],\n",
      "          [8.0478e-01, 5.5113e-01, 7.6869e-02, 3.9312e-01, 6.8340e-01],\n",
      "          [1.5086e-01, 6.4931e-01, 6.9271e-01, 5.1115e-01, 8.0203e-01],\n",
      "          [2.1308e-03, 8.5344e-01, 7.5243e-02, 1.2563e-02, 8.1082e-02],\n",
      "          [5.2202e-02, 9.3324e-01, 9.8174e-01, 3.2139e-01, 2.3517e-01]],\n",
      "\n",
      "         [[5.6920e-01, 9.2606e-02, 2.4761e-01, 2.7385e-01, 3.4965e-01],\n",
      "          [3.1914e-01, 3.0930e-01, 9.6457e-01, 6.2514e-01, 5.6342e-01],\n",
      "          [8.3573e-01, 4.0278e-01, 8.5270e-01, 5.0190e-01, 2.6032e-01],\n",
      "          [2.1723e-01, 1.3213e-01, 8.4565e-01, 7.2752e-01, 7.1806e-01],\n",
      "          [7.8828e-01, 6.4135e-01, 4.9430e-01, 1.5957e-01, 8.2387e-01]]],\n",
      "\n",
      "\n",
      "        [[[4.0049e-01, 7.2332e-01, 1.6266e-01, 6.1061e-01, 8.0607e-01],\n",
      "          [1.6676e-02, 2.9219e-01, 6.3301e-01, 3.2527e-01, 8.0562e-01],\n",
      "          [2.3614e-01, 7.8521e-01, 3.7544e-01, 8.9466e-02, 3.2582e-01],\n",
      "          [9.3473e-01, 5.9305e-01, 2.2367e-01, 2.8388e-01, 2.7867e-01],\n",
      "          [1.1091e-01, 1.1756e-01, 7.5985e-01, 9.9052e-01, 9.8480e-01]],\n",
      "\n",
      "         [[2.4231e-01, 9.5614e-01, 5.8175e-01, 9.9154e-01, 5.2499e-01],\n",
      "          [8.0769e-01, 8.3500e-01, 5.3525e-01, 4.1180e-01, 3.6425e-01],\n",
      "          [3.1377e-01, 1.7806e-01, 3.1943e-01, 8.7414e-02, 7.3267e-01],\n",
      "          [7.2980e-01, 3.2824e-01, 7.5369e-01, 9.8103e-01, 8.3205e-02],\n",
      "          [5.4276e-01, 1.8245e-01, 9.4141e-01, 8.6642e-01, 7.0643e-01]],\n",
      "\n",
      "         [[1.5498e-01, 4.7858e-01, 1.7662e-01, 7.8142e-01, 3.1374e-01],\n",
      "          [7.7358e-01, 5.3912e-01, 3.4175e-01, 3.4871e-01, 7.5010e-01],\n",
      "          [5.1298e-01, 3.7271e-02, 5.7167e-01, 9.2076e-01, 6.0703e-01],\n",
      "          [5.1379e-01, 4.5343e-01, 1.7061e-03, 7.3056e-01, 9.9156e-01],\n",
      "          [8.4512e-01, 7.6888e-01, 9.1336e-01, 5.9904e-01, 1.5574e-01]]],\n",
      "\n",
      "\n",
      "        [[[2.5098e-01, 4.5740e-01, 7.0392e-01, 2.3342e-01, 7.9021e-01],\n",
      "          [9.2922e-01, 9.3444e-01, 3.8113e-01, 4.2918e-01, 1.1710e-01],\n",
      "          [7.8220e-02, 3.8351e-01, 1.3231e-01, 4.9296e-01, 2.7519e-01],\n",
      "          [8.2840e-01, 8.3164e-01, 2.0035e-01, 4.9210e-01, 5.7407e-01],\n",
      "          [7.9656e-01, 8.9590e-01, 7.7738e-01, 7.9657e-01, 1.7669e-01]],\n",
      "\n",
      "         [[2.9980e-01, 4.6245e-01, 5.7826e-01, 7.9515e-01, 5.0103e-01],\n",
      "          [2.8556e-01, 6.0157e-01, 6.6597e-01, 7.5524e-01, 8.9823e-01],\n",
      "          [6.7760e-01, 4.9377e-01, 1.5193e-01, 9.6972e-01, 4.5252e-01],\n",
      "          [4.9676e-01, 5.6453e-01, 2.5788e-01, 7.3715e-01, 6.1538e-01],\n",
      "          [5.3513e-02, 8.5696e-01, 6.4420e-01, 5.8149e-01, 2.0586e-01]],\n",
      "\n",
      "         [[2.1108e-01, 4.2113e-01, 1.1433e-01, 7.3314e-01, 6.7432e-01],\n",
      "          [3.2373e-01, 2.6579e-01, 1.5200e-01, 8.6497e-01, 6.5118e-01],\n",
      "          [2.5968e-01, 5.4475e-01, 2.8252e-01, 4.5115e-01, 4.9991e-01],\n",
      "          [5.3571e-01, 6.6321e-03, 5.5280e-01, 7.0109e-01, 7.6575e-01],\n",
      "          [4.3048e-01, 1.4727e-01, 3.8713e-01, 7.0584e-01, 2.2234e-01]]],\n",
      "\n",
      "\n",
      "        [[[3.6686e-01, 3.4120e-02, 7.5650e-01, 5.5112e-01, 3.6382e-01],\n",
      "          [6.2837e-01, 9.8747e-01, 5.1709e-02, 4.9723e-01, 5.0575e-01],\n",
      "          [6.9619e-02, 8.3738e-01, 6.3991e-01, 3.3058e-01, 4.6331e-01],\n",
      "          [8.1667e-02, 1.0669e-01, 2.2148e-01, 8.5847e-01, 3.4390e-01],\n",
      "          [5.9774e-01, 7.5928e-01, 5.3651e-01, 9.0536e-01, 8.4991e-02]],\n",
      "\n",
      "         [[2.3170e-01, 3.8170e-01, 5.5888e-01, 4.7562e-01, 7.8463e-01],\n",
      "          [3.9649e-01, 3.8338e-01, 6.7530e-01, 4.9970e-01, 4.4195e-01],\n",
      "          [6.8553e-01, 2.2381e-01, 5.8746e-01, 3.5056e-01, 2.9770e-01],\n",
      "          [5.2727e-01, 9.5261e-03, 8.9818e-01, 9.5690e-01, 1.3905e-01],\n",
      "          [3.8869e-02, 8.9682e-01, 8.5285e-01, 4.1971e-01, 2.1377e-01]],\n",
      "\n",
      "         [[5.8808e-02, 2.5700e-01, 4.7391e-01, 3.5486e-01, 3.7080e-01],\n",
      "          [9.7945e-01, 4.8002e-01, 2.1439e-01, 8.5048e-01, 2.8831e-01],\n",
      "          [6.0679e-01, 1.0751e-01, 3.9514e-01, 3.3196e-01, 3.5665e-01],\n",
      "          [2.9026e-01, 1.6443e-02, 1.4212e-01, 1.4204e-01, 8.3769e-01],\n",
      "          [4.0980e-01, 9.7659e-01, 6.8901e-01, 7.4504e-01, 2.7141e-01]]],\n",
      "\n",
      "\n",
      "        [[[7.9973e-01, 1.5350e-01, 4.0433e-01, 5.6552e-01, 1.0872e-02],\n",
      "          [1.7528e-01, 2.0481e-01, 3.4278e-01, 3.7120e-01, 8.3720e-01],\n",
      "          [4.2566e-01, 4.8966e-01, 2.3699e-01, 8.9142e-01, 3.4500e-01],\n",
      "          [7.2408e-01, 3.2782e-01, 5.6308e-01, 4.0692e-01, 1.3614e-01],\n",
      "          [9.2280e-01, 1.6998e-01, 6.7800e-01, 6.5317e-01, 6.1783e-01]],\n",
      "\n",
      "         [[4.2294e-01, 8.8356e-01, 3.5439e-01, 8.0175e-01, 1.3101e-01],\n",
      "          [9.8968e-01, 1.3407e-01, 6.1647e-01, 3.4248e-01, 3.0180e-01],\n",
      "          [7.0512e-01, 2.0161e-01, 6.4844e-01, 7.5966e-01, 3.4549e-01],\n",
      "          [8.2400e-01, 3.3457e-01, 5.3833e-01, 6.9743e-01, 9.4119e-01],\n",
      "          [9.0450e-01, 3.0078e-01, 4.4557e-01, 3.0539e-01, 5.1223e-02]],\n",
      "\n",
      "         [[9.9777e-01, 3.3184e-01, 9.5364e-01, 6.9153e-01, 9.5217e-02],\n",
      "          [2.5901e-01, 9.6590e-01, 9.1839e-01, 5.7709e-01, 9.0912e-01],\n",
      "          [2.5597e-01, 4.2459e-01, 2.1920e-01, 9.6640e-01, 9.5580e-01],\n",
      "          [2.9461e-01, 6.3749e-02, 7.0330e-01, 4.5260e-01, 1.4334e-01],\n",
      "          [1.5578e-01, 5.7065e-01, 7.7608e-01, 5.2991e-01, 9.5215e-01]]]])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "a = torch.rand(10, 3, 5, 5)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.9836, 0.9357, 0.9902, 0.9997, 0.9932, 0.9817, 0.9916, 0.9697, 0.9875,\n",
      "        0.9978])\n"
     ]
    }
   ],
   "source": [
    "b = torch.linalg.vector_norm(a, ord=float('inf'), dim=(1, 2, 3))\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([10])\n"
     ]
    }
   ],
   "source": [
    "print(b.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[0.6703, 0.5436, 0.9081,  ..., 0.4246, 0.2476, 0.1485],\n",
      "         [0.2181, 0.3392, 0.3476,  ..., 0.0847, 0.6054, 0.4660],\n",
      "         [0.2984, 0.2908, 0.4550,  ..., 0.6588, 0.0918, 0.3592],\n",
      "         ...,\n",
      "         [0.1330, 0.4684, 0.6910,  ..., 0.3994, 0.6529, 0.5362],\n",
      "         [0.6637, 0.5499, 0.7322,  ..., 0.3117, 0.3997, 0.1955],\n",
      "         [0.1279, 0.9187, 0.9848,  ..., 0.5964, 0.1318, 0.5182]],\n",
      "\n",
      "        [[0.8127, 0.1990, 0.0273,  ..., 0.5969, 0.5417, 0.8310],\n",
      "         [0.7389, 0.4020, 0.2833,  ..., 0.5568, 0.9837, 0.6264],\n",
      "         [0.6933, 0.1308, 0.2537,  ..., 0.2222, 0.1711, 0.2640],\n",
      "         ...,\n",
      "         [0.0886, 0.6776, 0.5436,  ..., 0.7855, 0.9409, 0.4742],\n",
      "         [0.7438, 0.0240, 0.3978,  ..., 0.2507, 0.9063, 0.0896],\n",
      "         [0.9813, 0.7734, 0.2263,  ..., 0.4219, 0.2603, 0.8537]],\n",
      "\n",
      "        [[0.9037, 0.7371, 0.9091,  ..., 0.0469, 0.4552, 0.5574],\n",
      "         [0.8319, 0.4647, 0.6152,  ..., 0.2061, 0.8374, 0.6163],\n",
      "         [0.9234, 0.8480, 0.2354,  ..., 0.0987, 0.7493, 0.0948],\n",
      "         ...,\n",
      "         [0.3803, 0.3959, 0.7995,  ..., 0.7949, 0.5359, 0.6528],\n",
      "         [0.3023, 0.6609, 0.5185,  ..., 0.9078, 0.0559, 0.3499],\n",
      "         [0.7375, 0.3128, 0.8408,  ..., 0.2305, 0.0278, 0.2375]]])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "a = torch.rand((3, 32, 32))\n",
    "print(a)\n",
    "b = torch.rand((5, 32, 32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 32])\n",
      "torch.Size([32, 32])\n",
      "torch.Size([32, 32])\n",
      "torch.Size([5, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for item in a:\n",
    "    print(item.shape)\n",
    "    b += item\n",
    "print(b.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "dicter = {\n",
    "    '2-9': [],\n",
    "    '4-2': [],\n",
    "}\n",
    "\n",
    "flag1 = '2-9' in dicter.keys()\n",
    "flag2 = '3-1' in dicter.keys()\n",
    "print(flag1)\n",
    "print(flag2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.2182, 0.4466, 0.4188, 0.8317],\n",
      "        [0.8339, 0.9856, 0.9620, 0.6978],\n",
      "        [0.0274, 0.2471, 0.5379, 0.6981]])\n",
      "tensor([[0.5069, 0.8010, 0.8078, 0.7641]])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "a = torch.rand((3, 4))\n",
    "b = torch.rand((1, 4))\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.7251, 1.2476, 1.2266, 1.5958],\n",
      "        [1.3409, 1.7867, 1.7698, 1.4620],\n",
      "        [0.5344, 1.0481, 1.3457, 1.4622]])\n"
     ]
    }
   ],
   "source": [
    "print(a+b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 5, 9, 1, 2]\n"
     ]
    }
   ],
   "source": [
    "lister = [3, 5, 9, 1, 2]\n",
    "print(lister)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.1000, 0.2000, 0.3000],\n",
      "        [0.4000, 0.5000, 0.6000]])\n",
      "torch.Size([2, 3])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "a = torch.tensor([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]])\n",
    "print(a)\n",
    "print(a.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = a.mean(dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.2500, 0.3500, 0.4500])\n"
     ]
    }
   ],
   "source": [
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3])\n"
     ]
    }
   ],
   "source": [
    "print(b.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "\n",
    "    def hehe():\n",
    "        print(i)\n",
    "\n",
    "    hehe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "interger_numpy = np.random.randint(low=-1, high=2, size=(1, 28, 28))\n",
    "\n",
    "print(interger_numpy.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.4975, 0.5051, 0.9369, 0.2750],\n",
      "        [0.7841, 0.9049, 0.5929, 0.7704]])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "a = torch.rand((2, 4))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.4975, 0.5051],\n",
      "        [0.9369, 0.2750],\n",
      "        [0.7841, 0.9049],\n",
      "        [0.5929, 0.7704]])\n"
     ]
    }
   ],
   "source": [
    "b = a.reshape(4, 2)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.4975, 0.5051],\n",
      "        [0.9369, 0.2750],\n",
      "        [0.7841, 0.9049],\n",
      "        [0.5929, 0.7704]])\n",
      "tensor([[0.4975, 0.5051, 0.9369, 0.2750],\n",
      "        [0.7841, 0.9049, 0.5929, 0.7704]])\n"
     ]
    }
   ],
   "source": [
    "print(b)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.4975, 0.5051],\n",
      "        [0.9369, 0.2750],\n",
      "        [0.7841, 0.9049],\n",
      "        [0.5929, 0.7704]])\n"
     ]
    }
   ],
   "source": [
    "c = b.clone()\n",
    "\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.0000, 0.0000],\n",
      "        [0.9369, 0.2750],\n",
      "        [0.7841, 0.9049],\n",
      "        [0.5929, 0.7704]])\n"
     ]
    }
   ],
   "source": [
    "c[0] = 0.0\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.4975, 0.5051, 0.9369, 0.2750],\n",
      "        [0.7841, 0.9049, 0.5929, 0.7704]])\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.0000, 0.0000],\n",
      "        [0.9727, 0.9934],\n",
      "        [0.6609, 0.6216],\n",
      "        [0.1841, 0.8603]])\n"
     ]
    }
   ],
   "source": [
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.random.randint(low=-1, high=2)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.81311962]\n",
      "  [0.24880728]\n",
      "  [0.91036785]\n",
      "  ...\n",
      "  [0.61935737]\n",
      "  [0.9482345 ]\n",
      "  [0.23639944]]\n",
      "\n",
      " [[0.57775815]\n",
      "  [0.60960334]\n",
      "  [0.12704636]\n",
      "  ...\n",
      "  [0.66183752]\n",
      "  [0.59011078]\n",
      "  [0.68962733]]\n",
      "\n",
      " [[0.98058593]\n",
      "  [0.9472617 ]\n",
      "  [0.8978025 ]\n",
      "  ...\n",
      "  [0.5001459 ]\n",
      "  [0.28399122]\n",
      "  [0.0403005 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.69092216]\n",
      "  [0.25825227]\n",
      "  [0.41863677]\n",
      "  ...\n",
      "  [0.09368081]\n",
      "  [0.00853256]\n",
      "  [0.36108163]]\n",
      "\n",
      " [[0.10043004]\n",
      "  [0.82053154]\n",
      "  [0.60193437]\n",
      "  ...\n",
      "  [0.22806495]\n",
      "  [0.53279951]\n",
      "  [0.49310936]]\n",
      "\n",
      " [[0.31568576]\n",
      "  [0.09468446]\n",
      "  [0.26003505]\n",
      "  ...\n",
      "  [0.17255997]\n",
      "  [0.9450975 ]\n",
      "  [0.0198278 ]]]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "a = np.random.random((32, 32, 1))\n",
    "b = np.random.random((32, 32, 1))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x211074b4f10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(a)\n",
    "plt.imshow(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1]])\n",
      "torch.Size([1, 1])\n",
      "tensor([1])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "target_class = 1\n",
    "a = torch.tensor([[target_class]])\n",
    "print(a)\n",
    "print(a.shape)\n",
    "print(a[0:1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "import random \n",
    "import numpy as np\n",
    "\n",
    "# 0: 5\n",
    "# 2: 8\n",
    "# 4: 7\n",
    "\n",
    "np.random.seed(0)\n",
    "class_index = np.random.randint(0, 10)\n",
    "\n",
    "print(class_index)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 绘制非凸性图片 \n",
    "$\\delta \\in [0, 1]^{2}$， 通过PCA手段降维到两个维度，然后进行攻击\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0.生成数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 200)\n",
      "(200, 200, 4)\n",
      "[(127, 11349), (73, 11188), (162, 6083), (177, 5756), (126, 476), (71, 143), (70, 137), (69, 133), (72, 121), (40, 120), (64, 109), (53, 104), (58, 94), (67, 89), (68, 87), (62, 83), (60, 82), (63, 72), (96, 71), (161, 69), (55, 64), (50, 63), (125, 63), (49, 63), (45, 62), (47, 61), (46, 60), (155, 57), (51, 56), (57, 56), (66, 55), (176, 54), (56, 53), (117, 52), (52, 51), (65, 50), (142, 50), (35, 50), (48, 49), (122, 47), (54, 46), (59, 46), (42, 45), (170, 44), (123, 44), (113, 43), (38, 43), (37, 42), (33, 42), (77, 41), (111, 41), (61, 41), (44, 41), (91, 40), (43, 40), (75, 38), (100, 38), (121, 37), (98, 36), (130, 36), (79, 35), (124, 35), (39, 35), (34, 35), (108, 34), (41, 34), (116, 33), (158, 32), (156, 32), (102, 32), (36, 31), (160, 29), (95, 28), (101, 28), (81, 27), (83, 27), (105, 27), (112, 26), (152, 26), (93, 26), (80, 25), (114, 23), (118, 23), (119, 23), (157, 23), (159, 23), (31, 23), (135, 22), (115, 22), (82, 22), (85, 21), (154, 21), (76, 20), (120, 20), (32, 20), (84, 20), (88, 19), (74, 19), (104, 19), (149, 19), (146, 19), (94, 18), (153, 18), (87, 18), (97, 18), (78, 18), (103, 18), (109, 17), (86, 17), (150, 17), (106, 17), (92, 17), (128, 17), (147, 16), (30, 16), (90, 16), (165, 16), (141, 15), (175, 15), (171, 14), (107, 14), (89, 14), (138, 13), (133, 13), (140, 12), (172, 12), (99, 12), (174, 12), (28, 12), (145, 12), (110, 11), (129, 11), (169, 11), (132, 11), (173, 11), (134, 11), (143, 11), (167, 10), (148, 10), (137, 9), (139, 8), (29, 8), (144, 8), (164, 7), (163, 7), (168, 7), (136, 7), (27, 6), (151, 6), (131, 3), (25, 3), (166, 2), (16, 2), (15, 2), (9, 2), (24, 1), (11, 1), (14, 1), (19, 1), (26, 1), (22, 1), (18, 1), (20, 1)]\n",
      "[127, 73, 162, 177]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mping\n",
    "from PIL import Image\n",
    "from torchvision import transforms as T\n",
    "\n",
    "img_path = './img/intro/new.png'\n",
    "\n",
    "img = Image.open(img_path)\n",
    "trans_img = T.Resize((200, 200))(img)\n",
    "print(trans_img.size)\n",
    "plt.imshow(trans_img)\n",
    "\n",
    "## trans to array\n",
    "img_numpy = np.array(trans_img)\n",
    "print(img_numpy.shape)\n",
    "\n",
    "dicter = {}\n",
    "\n",
    "for i in range(200):\n",
    "    for j in range(200):\n",
    "        index = img_numpy[i, j, 1]\n",
    "        if index not in dicter.keys():\n",
    "            dicter[index] = 1\n",
    "        else:\n",
    "            dicter[index] += 1\n",
    "\n",
    "## print(dicter)\n",
    "\n",
    "dicter_sort = sorted(dicter.items(), key=lambda x: x[1], reverse=True)\n",
    "\n",
    "print(dicter_sort)\n",
    "\n",
    "lister = [dicter_sort[i][0] for i in range(4)]\n",
    "\n",
    "print(lister)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "## 随机采样 5000个点\n",
    "total_number = 0\n",
    "\n",
    "point_x_list = []\n",
    "point_y_list = []\n",
    "point_label_list = []\n",
    "while True:\n",
    "\n",
    "    if total_number == 5000:\n",
    "        break\n",
    "    x = random.randint(0, 199)\n",
    "    y = random.randint(0, 199)\n",
    "    value = img_numpy[x, y, 1]\n",
    "\n",
    "    if value not in lister:\n",
    "        continue\n",
    "    else:\n",
    "        total_number += 1\n",
    "        point_x_list.append(x / 200)\n",
    "        point_y_list.append(y / 200)\n",
    "        for index, item in enumerate(lister):\n",
    "            if value == item:\n",
    "                point_label_list.append(index)\n",
    "\n",
    "# color_list = []\n",
    "\n",
    "# for value in point_label_list:\n",
    "#     if value == 0:\n",
    "#         color_list.append('r')\n",
    "#     elif value == 1:\n",
    "#         color_list.append('b')\n",
    "#     elif value == 2:\n",
    "#         color_list.append('green')\n",
    "#     else:\n",
    "#         color_list.append('yellow')\n",
    "\n",
    "\n",
    "plt.scatter(point_x_list, point_y_list, c=point_label_list)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 制作数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5000, 2])\n",
      "torch.Size([5000])\n",
      "train size : 4000\n",
      "test size : 1000\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch\n",
    "\n",
    "data_x = torch.tensor(point_x_list)\n",
    "data_y = torch.tensor(point_y_list)\n",
    "data = torch.stack([data_x, data_y], dim=1)\n",
    "label = torch.tensor(point_label_list)\n",
    "print(data.shape)\n",
    "print(label.shape)\n",
    "\n",
    "train_data = data[:4000]\n",
    "train_label = label[:4000]\n",
    "\n",
    "test_data = data[4000:]\n",
    "test_label = label[4000:]\n",
    "\n",
    "# for item in label:\n",
    "\n",
    "#     print(item, end=' ')\n",
    "\n",
    "class MyDataset(Dataset):\n",
    "\n",
    "    def __init__(self, data, label):\n",
    "        \n",
    "        self.data = data\n",
    "        self.label = label\n",
    "\n",
    "    def __len__(self, ):\n",
    "\n",
    "        return len(self.data)\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "\n",
    "        return self.data[index], self.label[index]\n",
    "\n",
    "\n",
    "train_dataset = MyDataset(data=train_data, label=train_label)\n",
    "train_loader = DataLoader(dataset=train_dataset, batch_size=128, shuffle=True)\n",
    "\n",
    "test_dataset = MyDataset(data=test_data, label=test_label)\n",
    "test_loader = DataLoader(dataset=test_dataset, batch_size=100, shuffle=False)\n",
    "\n",
    "train_size = 0\n",
    "test_size = 0\n",
    "\n",
    "for image, target in train_loader:\n",
    "\n",
    "    train_size += image.shape[0]\n",
    "\n",
    "for image, target in test_loader:\n",
    "\n",
    "    test_size += image.shape[0]\n",
    "\n",
    "print('train size : {}'.format(train_size))\n",
    "print('test size : {}'.format(test_size))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class MLP(nn.Module): \n",
    "    def __init__(self): \n",
    "        super(MLP, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features=2, out_features=50)\n",
    "        self.linear2 = nn.Linear(in_features=50, out_features=50)\n",
    "        self.linear3 = nn.Linear(in_features=50, out_features=4)\n",
    "\n",
    "    def forward(self, x): \n",
    "        x = self.linear1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.linear2(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.linear3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch : 0, Trian Loss : 42.9754\n",
      "Epoch : 1, Trian Loss : 42.4789\n",
      "Epoch : 2, Trian Loss : 41.9933\n",
      "Epoch : 3, Trian Loss : 41.6540\n",
      "Epoch : 4, Trian Loss : 41.2767\n",
      "Epoch : 5, Trian Loss : 40.8078\n",
      "Epoch : 6, Trian Loss : 40.5125\n",
      "Epoch : 7, Trian Loss : 40.1796\n",
      "Epoch : 8, Trian Loss : 39.9637\n",
      "Epoch : 9, Trian Loss : 39.7031\n",
      "Epoch : 10, Trian Loss : 39.6210\n",
      "Epoch : 11, Trian Loss : 39.3407\n",
      "Epoch : 12, Trian Loss : 39.2493\n",
      "Epoch : 13, Trian Loss : 39.0729\n",
      "Epoch : 14, Trian Loss : 38.8797\n",
      "Epoch : 15, Trian Loss : 38.6940\n",
      "Epoch : 16, Trian Loss : 38.5381\n",
      "Epoch : 17, Trian Loss : 38.3628\n",
      "Epoch : 18, Trian Loss : 38.1546\n",
      "Epoch : 19, Trian Loss : 37.8578\n",
      "Epoch : 20, Trian Loss : 37.7315\n",
      "Epoch : 21, Trian Loss : 37.5444\n",
      "Epoch : 22, Trian Loss : 37.2581\n",
      "Epoch : 23, Trian Loss : 37.0273\n",
      "Epoch : 24, Trian Loss : 36.9847\n",
      "Epoch : 25, Trian Loss : 36.7224\n",
      "Epoch : 26, Trian Loss : 36.4860\n",
      "Epoch : 27, Trian Loss : 36.1514\n",
      "Epoch : 28, Trian Loss : 35.9961\n",
      "Epoch : 29, Trian Loss : 35.7808\n",
      "Epoch : 30, Trian Loss : 35.5387\n",
      "Epoch : 31, Trian Loss : 35.3365\n",
      "Epoch : 32, Trian Loss : 34.9819\n",
      "Epoch : 33, Trian Loss : 34.8239\n",
      "Epoch : 34, Trian Loss : 34.5281\n",
      "Epoch : 35, Trian Loss : 34.2986\n",
      "Epoch : 36, Trian Loss : 34.1792\n",
      "Epoch : 37, Trian Loss : 33.7521\n",
      "Epoch : 38, Trian Loss : 33.6433\n",
      "Epoch : 39, Trian Loss : 33.4284\n",
      "Epoch : 40, Trian Loss : 33.0022\n",
      "Epoch : 41, Trian Loss : 32.8359\n",
      "Epoch : 42, Trian Loss : 32.6327\n",
      "Epoch : 43, Trian Loss : 32.5897\n",
      "Epoch : 44, Trian Loss : 32.2336\n",
      "Epoch : 45, Trian Loss : 32.1005\n",
      "Epoch : 46, Trian Loss : 32.1340\n",
      "Epoch : 47, Trian Loss : 31.8352\n",
      "Epoch : 48, Trian Loss : 31.9357\n",
      "Epoch : 49, Trian Loss : 31.6203\n",
      "Epoch : 50, Trian Loss : 31.5752\n",
      "Epoch : 51, Trian Loss : 31.3591\n",
      "Epoch : 52, Trian Loss : 31.1570\n",
      "Epoch : 53, Trian Loss : 30.8364\n",
      "Epoch : 54, Trian Loss : 30.8346\n",
      "Epoch : 55, Trian Loss : 30.7850\n",
      "Epoch : 56, Trian Loss : 30.5996\n",
      "Epoch : 57, Trian Loss : 30.8733\n",
      "Epoch : 58, Trian Loss : 30.6448\n",
      "Epoch : 59, Trian Loss : 30.5021\n",
      "Epoch : 60, Trian Loss : 30.2455\n",
      "Epoch : 61, Trian Loss : 29.9497\n",
      "Epoch : 62, Trian Loss : 30.0124\n",
      "Epoch : 63, Trian Loss : 29.9056\n",
      "Epoch : 64, Trian Loss : 29.6261\n",
      "Epoch : 65, Trian Loss : 30.4951\n",
      "Epoch : 66, Trian Loss : 29.5563\n",
      "Epoch : 67, Trian Loss : 29.2759\n",
      "Epoch : 68, Trian Loss : 29.2520\n",
      "Epoch : 69, Trian Loss : 30.0516\n",
      "Epoch : 70, Trian Loss : 28.8450\n",
      "Epoch : 71, Trian Loss : 29.0243\n",
      "Epoch : 72, Trian Loss : 29.9865\n",
      "Epoch : 73, Trian Loss : 29.1504\n",
      "Epoch : 74, Trian Loss : 29.1318\n",
      "Epoch : 75, Trian Loss : 28.6661\n",
      "Epoch : 76, Trian Loss : 28.5522\n",
      "Epoch : 77, Trian Loss : 28.7574\n",
      "Epoch : 78, Trian Loss : 28.5368\n",
      "Epoch : 79, Trian Loss : 28.2435\n",
      "Epoch : 80, Trian Loss : 28.7023\n",
      "Epoch : 81, Trian Loss : 28.5084\n",
      "Epoch : 82, Trian Loss : 28.1882\n",
      "Epoch : 83, Trian Loss : 28.2120\n",
      "Epoch : 84, Trian Loss : 28.3559\n",
      "Epoch : 85, Trian Loss : 28.0489\n",
      "Epoch : 86, Trian Loss : 28.5954\n",
      "Epoch : 87, Trian Loss : 28.2627\n",
      "Epoch : 88, Trian Loss : 27.7710\n",
      "Epoch : 89, Trian Loss : 27.7774\n",
      "Epoch : 90, Trian Loss : 27.4426\n",
      "Epoch : 91, Trian Loss : 27.4118\n",
      "Epoch : 92, Trian Loss : 27.1647\n",
      "Epoch : 93, Trian Loss : 27.1263\n",
      "Epoch : 94, Trian Loss : 26.9897\n",
      "Epoch : 95, Trian Loss : 27.1166\n",
      "Epoch : 96, Trian Loss : 27.2177\n",
      "Epoch : 97, Trian Loss : 27.1365\n",
      "Epoch : 98, Trian Loss : 27.1905\n",
      "Epoch : 99, Trian Loss : 27.5391\n",
      "Epoch : 100, Trian Loss : 27.6298\n",
      "Epoch : 101, Trian Loss : 27.1952\n",
      "Epoch : 102, Trian Loss : 26.5213\n",
      "Epoch : 103, Trian Loss : 27.9088\n",
      "Epoch : 104, Trian Loss : 26.0498\n",
      "Epoch : 105, Trian Loss : 26.5692\n",
      "Epoch : 106, Trian Loss : 26.5621\n",
      "Epoch : 107, Trian Loss : 26.5447\n",
      "Epoch : 108, Trian Loss : 26.3148\n",
      "Epoch : 109, Trian Loss : 26.2301\n",
      "Epoch : 110, Trian Loss : 26.1131\n",
      "Epoch : 111, Trian Loss : 26.0308\n",
      "Epoch : 112, Trian Loss : 26.2950\n",
      "Epoch : 113, Trian Loss : 26.4907\n",
      "Epoch : 114, Trian Loss : 25.6723\n",
      "Epoch : 115, Trian Loss : 25.2563\n",
      "Epoch : 116, Trian Loss : 25.1768\n",
      "Epoch : 117, Trian Loss : 25.4135\n",
      "Epoch : 118, Trian Loss : 25.3322\n",
      "Epoch : 119, Trian Loss : 25.0541\n",
      "Epoch : 120, Trian Loss : 25.0541\n",
      "Epoch : 121, Trian Loss : 25.1528\n",
      "Epoch : 122, Trian Loss : 25.3234\n",
      "Epoch : 123, Trian Loss : 25.7619\n",
      "Epoch : 124, Trian Loss : 24.9521\n",
      "Epoch : 125, Trian Loss : 25.3850\n",
      "Epoch : 126, Trian Loss : 24.6965\n",
      "Epoch : 127, Trian Loss : 24.8519\n",
      "Epoch : 128, Trian Loss : 24.1052\n",
      "Epoch : 129, Trian Loss : 25.5926\n",
      "Epoch : 130, Trian Loss : 25.1565\n",
      "Epoch : 131, Trian Loss : 24.7971\n",
      "Epoch : 132, Trian Loss : 25.1168\n",
      "Epoch : 133, Trian Loss : 24.2832\n",
      "Epoch : 134, Trian Loss : 24.2272\n",
      "Epoch : 135, Trian Loss : 24.3169\n",
      "Epoch : 136, Trian Loss : 24.7126\n",
      "Epoch : 137, Trian Loss : 24.7399\n",
      "Epoch : 138, Trian Loss : 24.3365\n",
      "Epoch : 139, Trian Loss : 24.2475\n",
      "Epoch : 140, Trian Loss : 24.3360\n",
      "Epoch : 141, Trian Loss : 24.3634\n",
      "Epoch : 142, Trian Loss : 24.7429\n",
      "Epoch : 143, Trian Loss : 24.1261\n",
      "Epoch : 144, Trian Loss : 24.5874\n",
      "Epoch : 145, Trian Loss : 23.4620\n",
      "Epoch : 146, Trian Loss : 24.7339\n",
      "Epoch : 147, Trian Loss : 23.6570\n",
      "Epoch : 148, Trian Loss : 23.8310\n",
      "Epoch : 149, Trian Loss : 22.4654\n",
      "Epoch : 150, Trian Loss : 24.8547\n",
      "Epoch : 151, Trian Loss : 23.0074\n",
      "Epoch : 152, Trian Loss : 23.2824\n",
      "Epoch : 153, Trian Loss : 22.4998\n",
      "Epoch : 154, Trian Loss : 23.4273\n",
      "Epoch : 155, Trian Loss : 23.8598\n",
      "Epoch : 156, Trian Loss : 22.8050\n",
      "Epoch : 157, Trian Loss : 23.4780\n",
      "Epoch : 158, Trian Loss : 22.8538\n",
      "Epoch : 159, Trian Loss : 21.9783\n",
      "Epoch : 160, Trian Loss : 22.1728\n",
      "Epoch : 161, Trian Loss : 24.3732\n",
      "Epoch : 162, Trian Loss : 21.8772\n",
      "Epoch : 163, Trian Loss : 22.4536\n",
      "Epoch : 164, Trian Loss : 21.9192\n",
      "Epoch : 165, Trian Loss : 21.7615\n",
      "Epoch : 166, Trian Loss : 23.1911\n",
      "Epoch : 167, Trian Loss : 21.8859\n",
      "Epoch : 168, Trian Loss : 23.4862\n",
      "Epoch : 169, Trian Loss : 22.7228\n",
      "Epoch : 170, Trian Loss : 22.1018\n",
      "Epoch : 171, Trian Loss : 21.7096\n",
      "Epoch : 172, Trian Loss : 21.1023\n",
      "Epoch : 173, Trian Loss : 21.5073\n",
      "Epoch : 174, Trian Loss : 21.6292\n",
      "Epoch : 175, Trian Loss : 22.2274\n",
      "Epoch : 176, Trian Loss : 20.9821\n",
      "Epoch : 177, Trian Loss : 20.7916\n",
      "Epoch : 178, Trian Loss : 20.6058\n",
      "Epoch : 179, Trian Loss : 21.8144\n",
      "Epoch : 180, Trian Loss : 20.7627\n",
      "Epoch : 181, Trian Loss : 21.5140\n",
      "Epoch : 182, Trian Loss : 21.3020\n",
      "Epoch : 183, Trian Loss : 22.6101\n",
      "Epoch : 184, Trian Loss : 21.4368\n",
      "Epoch : 185, Trian Loss : 21.0189\n",
      "Epoch : 186, Trian Loss : 20.5347\n",
      "Epoch : 187, Trian Loss : 20.1011\n",
      "Epoch : 188, Trian Loss : 20.4004\n",
      "Epoch : 189, Trian Loss : 19.8130\n",
      "Epoch : 190, Trian Loss : 20.2037\n",
      "Epoch : 191, Trian Loss : 19.5270\n",
      "Epoch : 192, Trian Loss : 19.4156\n",
      "Epoch : 193, Trian Loss : 19.8980\n",
      "Epoch : 194, Trian Loss : 19.5449\n",
      "Epoch : 195, Trian Loss : 19.7007\n",
      "Epoch : 196, Trian Loss : 20.8974\n",
      "Epoch : 197, Trian Loss : 19.0541\n",
      "Epoch : 198, Trian Loss : 19.9645\n",
      "Epoch : 199, Trian Loss : 19.4799\n",
      "Epoch : 200, Trian Loss : 17.7206\n",
      "Epoch : 201, Trian Loss : 17.1246\n",
      "Epoch : 202, Trian Loss : 17.1256\n",
      "Epoch : 203, Trian Loss : 16.9708\n",
      "Epoch : 204, Trian Loss : 17.1352\n",
      "Epoch : 205, Trian Loss : 17.0584\n",
      "Epoch : 206, Trian Loss : 17.0137\n",
      "Epoch : 207, Trian Loss : 16.9609\n",
      "Epoch : 208, Trian Loss : 16.9170\n",
      "Epoch : 209, Trian Loss : 17.0336\n",
      "Epoch : 210, Trian Loss : 17.1214\n",
      "Epoch : 211, Trian Loss : 16.9401\n",
      "Epoch : 212, Trian Loss : 16.9766\n",
      "Epoch : 213, Trian Loss : 16.8979\n",
      "Epoch : 214, Trian Loss : 16.8938\n",
      "Epoch : 215, Trian Loss : 16.9467\n",
      "Epoch : 216, Trian Loss : 16.9652\n",
      "Epoch : 217, Trian Loss : 17.0008\n",
      "Epoch : 218, Trian Loss : 16.8325\n",
      "Epoch : 219, Trian Loss : 16.9562\n",
      "Epoch : 220, Trian Loss : 16.7958\n",
      "Epoch : 221, Trian Loss : 16.8023\n",
      "Epoch : 222, Trian Loss : 16.8235\n",
      "Epoch : 223, Trian Loss : 16.9024\n",
      "Epoch : 224, Trian Loss : 16.7670\n",
      "Epoch : 225, Trian Loss : 16.8012\n",
      "Epoch : 226, Trian Loss : 16.7502\n",
      "Epoch : 227, Trian Loss : 16.7154\n",
      "Epoch : 228, Trian Loss : 16.7373\n",
      "Epoch : 229, Trian Loss : 16.6459\n",
      "Epoch : 230, Trian Loss : 16.7613\n",
      "Epoch : 231, Trian Loss : 16.8210\n",
      "Epoch : 232, Trian Loss : 16.6118\n",
      "Epoch : 233, Trian Loss : 16.6644\n",
      "Epoch : 234, Trian Loss : 16.5505\n",
      "Epoch : 235, Trian Loss : 16.6387\n",
      "Epoch : 236, Trian Loss : 16.7899\n",
      "Epoch : 237, Trian Loss : 16.5434\n",
      "Epoch : 238, Trian Loss : 16.6159\n",
      "Epoch : 239, Trian Loss : 16.6701\n",
      "Epoch : 240, Trian Loss : 16.5896\n",
      "Epoch : 241, Trian Loss : 16.4638\n",
      "Epoch : 242, Trian Loss : 16.6076\n",
      "Epoch : 243, Trian Loss : 16.5886\n",
      "Epoch : 244, Trian Loss : 16.5066\n",
      "Epoch : 245, Trian Loss : 16.5594\n",
      "Epoch : 246, Trian Loss : 16.4692\n",
      "Epoch : 247, Trian Loss : 16.5253\n",
      "Epoch : 248, Trian Loss : 16.4416\n",
      "Epoch : 249, Trian Loss : 16.5605\n",
      "Epoch : 250, Trian Loss : 16.5707\n",
      "Epoch : 251, Trian Loss : 16.3989\n",
      "Epoch : 252, Trian Loss : 16.5196\n",
      "Epoch : 253, Trian Loss : 16.4978\n",
      "Epoch : 254, Trian Loss : 16.4759\n",
      "Epoch : 255, Trian Loss : 16.3694\n",
      "Epoch : 256, Trian Loss : 16.4875\n",
      "Epoch : 257, Trian Loss : 16.4246\n",
      "Epoch : 258, Trian Loss : 16.4091\n",
      "Epoch : 259, Trian Loss : 16.3287\n",
      "Epoch : 260, Trian Loss : 16.3476\n",
      "Epoch : 261, Trian Loss : 16.4532\n",
      "Epoch : 262, Trian Loss : 16.3554\n",
      "Epoch : 263, Trian Loss : 16.2766\n",
      "Epoch : 264, Trian Loss : 16.2800\n",
      "Epoch : 265, Trian Loss : 16.2106\n",
      "Epoch : 266, Trian Loss : 16.2195\n",
      "Epoch : 267, Trian Loss : 16.2494\n",
      "Epoch : 268, Trian Loss : 16.3446\n",
      "Epoch : 269, Trian Loss : 16.4419\n",
      "Epoch : 270, Trian Loss : 16.3113\n",
      "Epoch : 271, Trian Loss : 16.2533\n",
      "Epoch : 272, Trian Loss : 16.1905\n",
      "Epoch : 273, Trian Loss : 16.2996\n",
      "Epoch : 274, Trian Loss : 16.3493\n",
      "Epoch : 275, Trian Loss : 16.1436\n",
      "Epoch : 276, Trian Loss : 16.2906\n",
      "Epoch : 277, Trian Loss : 16.2449\n",
      "Epoch : 278, Trian Loss : 16.1219\n",
      "Epoch : 279, Trian Loss : 16.1788\n",
      "Epoch : 280, Trian Loss : 16.0944\n",
      "Epoch : 281, Trian Loss : 16.1194\n",
      "Epoch : 282, Trian Loss : 16.1640\n",
      "Epoch : 283, Trian Loss : 16.1254\n",
      "Epoch : 284, Trian Loss : 16.0099\n",
      "Epoch : 285, Trian Loss : 15.9503\n",
      "Epoch : 286, Trian Loss : 15.9712\n",
      "Epoch : 287, Trian Loss : 16.0650\n",
      "Epoch : 288, Trian Loss : 16.0785\n",
      "Epoch : 289, Trian Loss : 15.9641\n",
      "Epoch : 290, Trian Loss : 16.2208\n",
      "Epoch : 291, Trian Loss : 15.9973\n",
      "Epoch : 292, Trian Loss : 16.1849\n",
      "Epoch : 293, Trian Loss : 16.0797\n",
      "Epoch : 294, Trian Loss : 16.0534\n",
      "Epoch : 295, Trian Loss : 16.1226\n",
      "Epoch : 296, Trian Loss : 16.0315\n",
      "Epoch : 297, Trian Loss : 15.8685\n",
      "Epoch : 298, Trian Loss : 16.1472\n",
      "Epoch : 299, Trian Loss : 15.8737\n"
     ]
    }
   ],
   "source": [
    "device = 'cuda'\n",
    "\n",
    "model = MLP().to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
    "\n",
    "for epoch in range(300):\n",
    "\n",
    "    train_loss = 0.0\n",
    "\n",
    "    if epoch == 200:\n",
    "        optimizer.param_groups[0]['lr'] *= 0.1\n",
    "    \n",
    "    for image, target in train_loader:\n",
    "\n",
    "        image = image.to(device)\n",
    "        target = target.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        output = model(image)\n",
    "\n",
    "        loss = criterion(output, target.long())\n",
    "        train_loss += loss.item()\n",
    "\n",
    "        loss.backward()\n",
    "\n",
    "        optimizer.step()\n",
    "\n",
    "    print('Epoch : {}, Trian Loss : {:.4f}'.format(epoch, train_loss))\n",
    "\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====> Test set acc: 82.20%\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model.eval()\n",
    "test_loss= 0\n",
    "total = 0\n",
    "correct = 0\n",
    "with torch.no_grad():\n",
    "    for image, target in test_loader:\n",
    "        image, target = image.to(device), target.to(device)        \n",
    "        output = model(image)\n",
    "        \n",
    "        _, pred = torch.max(output.data, 1)\n",
    "        \n",
    "        total += image.shape[0]\n",
    "        correct += (pred == target).sum()\n",
    "\n",
    "print('====> Test set acc: {:.2f}%'.format(correct / total * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23038/3974254918.py:62: UserWarning: The following kwargs were not used by contour: 'color'\n",
      "  con = ax.contour(x_grid, y_grid, function(x_grid, y_grid), 6, color='black')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Save 92\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import cm\n",
    "arr = [92, ]\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "for dex in arr:\n",
    "\n",
    "    pass_loader = DataLoader(test_dataset, batch_size=1)\n",
    "    sample_data = None \n",
    "    sample_label = None\n",
    "\n",
    "    for index, (a, b) in enumerate(pass_loader):\n",
    "\n",
    "        sample_data = a\n",
    "        sample_label = b\n",
    "\n",
    "        ## default 2\n",
    "        ## default 8\n",
    "        ## default 18\n",
    "        if index == dex:\n",
    "            break\n",
    "\n",
    "    sample_data = sample_data.to(device)\n",
    "    sample_label = sample_label.to(device)\n",
    "\n",
    "    softmax_layer = nn.Softmax(dim=1)\n",
    "\n",
    "    def function(x, y):\n",
    "        \n",
    "        x = torch.tensor(x).reshape(-1)\n",
    "        y = torch.tensor(y).reshape(-1)\n",
    "        delta = torch.stack([x, y], dim=1).to(device, dtype=torch.float)\n",
    "\n",
    "        adv_sample_data = torch.clamp(sample_data + delta, min=0.0, max=1.0)\n",
    "\n",
    "        output = model(adv_sample_data)\n",
    "\n",
    "        pred = softmax_layer(output)\n",
    "\n",
    "        ret = -pred.detach().cpu().numpy() + 1.0\n",
    "        ret = ret[:, sample_label[0]]\n",
    "        ret = ret.reshape(128, 128)\n",
    "\n",
    "        return ret\n",
    "\n",
    "\n",
    "    n = 128\n",
    "\n",
    "    x = np.linspace(-0.5, 0.5, n)\n",
    "    y = np.linspace(-0.5, 0.5, n)\n",
    "\n",
    "\n",
    "    x_grid, y_grid = np.meshgrid(x, y)\n",
    "\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "\n",
    "    ## copper summer pink hot\n",
    "    # con = ax.contour(x_grid, y_grid, function(x_grid, y_grid), 6, color='black')\n",
    "    # ax.contourf(x_grid, y_grid, function(x_grid, y_grid), 6, alpha=0.5, cmap=plt.cm.copper)\n",
    "    con = ax.contour(x_grid, y_grid, function(x_grid, y_grid), 6, color='black')\n",
    "    ax.contourf(x_grid, y_grid, function(x_grid, y_grid), 6, alpha=0.5, cmap=plt.cm.pink)\n",
    "    ax.clabel(con,inline=True, fmt='%1.1f', fontsize=10)\n",
    "    plt.xlabel('$\\delta 1$')\n",
    "    plt.ylabel('$\\delta 2$')\n",
    "    ## plt.savefig('./img/temp/{}.pdf'.format(dex), dpi=800, bbox_inches='tight', pad_inches=0.2)\n",
    "    plt.savefig('./img/temp/{}.pdf'.format(dex), dpi=800)\n",
    "    print('Save {}'.format(dex))\n",
    "    plt.close()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.搭建一个网络，输入维度是两维，输出是个二维的分类任务"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 128)\n",
      "(128, 128)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23038/1564172589.py:20: UserWarning: The following kwargs were not used by contour: 'color'\n",
      "  con = ax.contour(x_grid, y_grid, function(x_grid, y_grid), 6, color='black')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def func(x, y):\n",
    "    # x = deltas[0]\n",
    "    # y = deltas[1]\n",
    "    return x**3 + y**3 \n",
    "\n",
    "n = 128\n",
    "\n",
    "x = np.linspace(-0.6, 0.6, n)\n",
    "y = np.linspace(-0.6, 0.6, n)\n",
    "\n",
    "\n",
    "x_grid, y_grid = np.meshgrid(x, y)\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "con = ax.contour(x_grid, y_grid, function(x_grid, y_grid), 6, color='black')\n",
    "ax.contourf(x_grid, y_grid, function(x_grid, y_grid), 6, alpha=0.5, cmap=plt.cm.copper)\n",
    "ax.clabel(con,inline=True, fmt='%1.1f', fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[0.64597788 0.01860882 0.11331961]\n",
      "   [0.02971017 0.97005801 0.15154505]\n",
      "   [0.94043089 0.26700968 0.97520421]]\n",
      "\n",
      "  [[0.70473668 0.94749966 0.38635681]\n",
      "   [0.17398425 0.62440501 0.98158073]\n",
      "   [0.47141499 0.20727288 0.33668275]]\n",
      "\n",
      "  [[0.07015291 0.85538039 0.33043596]\n",
      "   [0.30407006 0.71731631 0.23962045]\n",
      "   [0.66582067 0.51900469 0.60774608]]]]\n",
      "(1, 3, 9, 9)\n",
      "[[[[0.64597788 0.64597788 0.64597788 0.01860882 0.01860882 0.01860882\n",
      "    0.11331961 0.11331961 0.11331961]\n",
      "   [0.64597788 0.64597788 0.64597788 0.01860882 0.01860882 0.01860882\n",
      "    0.11331961 0.11331961 0.11331961]\n",
      "   [0.64597788 0.64597788 0.64597788 0.01860882 0.01860882 0.01860882\n",
      "    0.11331961 0.11331961 0.11331961]\n",
      "   [0.02971017 0.02971017 0.02971017 0.97005801 0.97005801 0.97005801\n",
      "    0.15154505 0.15154505 0.15154505]\n",
      "   [0.02971017 0.02971017 0.02971017 0.97005801 0.97005801 0.97005801\n",
      "    0.15154505 0.15154505 0.15154505]\n",
      "   [0.02971017 0.02971017 0.02971017 0.97005801 0.97005801 0.97005801\n",
      "    0.15154505 0.15154505 0.15154505]\n",
      "   [0.94043089 0.94043089 0.94043089 0.26700968 0.26700968 0.26700968\n",
      "    0.97520421 0.97520421 0.97520421]\n",
      "   [0.94043089 0.94043089 0.94043089 0.26700968 0.26700968 0.26700968\n",
      "    0.97520421 0.97520421 0.97520421]\n",
      "   [0.94043089 0.94043089 0.94043089 0.26700968 0.26700968 0.26700968\n",
      "    0.97520421 0.97520421 0.97520421]]\n",
      "\n",
      "  [[0.70473668 0.70473668 0.70473668 0.94749966 0.94749966 0.94749966\n",
      "    0.38635681 0.38635681 0.38635681]\n",
      "   [0.70473668 0.70473668 0.70473668 0.94749966 0.94749966 0.94749966\n",
      "    0.38635681 0.38635681 0.38635681]\n",
      "   [0.70473668 0.70473668 0.70473668 0.94749966 0.94749966 0.94749966\n",
      "    0.38635681 0.38635681 0.38635681]\n",
      "   [0.17398425 0.17398425 0.17398425 0.62440501 0.62440501 0.62440501\n",
      "    0.98158073 0.98158073 0.98158073]\n",
      "   [0.17398425 0.17398425 0.17398425 0.62440501 0.62440501 0.62440501\n",
      "    0.98158073 0.98158073 0.98158073]\n",
      "   [0.17398425 0.17398425 0.17398425 0.62440501 0.62440501 0.62440501\n",
      "    0.98158073 0.98158073 0.98158073]\n",
      "   [0.47141499 0.47141499 0.47141499 0.20727288 0.20727288 0.20727288\n",
      "    0.33668275 0.33668275 0.33668275]\n",
      "   [0.47141499 0.47141499 0.47141499 0.20727288 0.20727288 0.20727288\n",
      "    0.33668275 0.33668275 0.33668275]\n",
      "   [0.47141499 0.47141499 0.47141499 0.20727288 0.20727288 0.20727288\n",
      "    0.33668275 0.33668275 0.33668275]]\n",
      "\n",
      "  [[0.07015291 0.07015291 0.07015291 0.85538039 0.85538039 0.85538039\n",
      "    0.33043596 0.33043596 0.33043596]\n",
      "   [0.07015291 0.07015291 0.07015291 0.85538039 0.85538039 0.85538039\n",
      "    0.33043596 0.33043596 0.33043596]\n",
      "   [0.07015291 0.07015291 0.07015291 0.85538039 0.85538039 0.85538039\n",
      "    0.33043596 0.33043596 0.33043596]\n",
      "   [0.30407006 0.30407006 0.30407006 0.71731631 0.71731631 0.71731631\n",
      "    0.23962045 0.23962045 0.23962045]\n",
      "   [0.30407006 0.30407006 0.30407006 0.71731631 0.71731631 0.71731631\n",
      "    0.23962045 0.23962045 0.23962045]\n",
      "   [0.30407006 0.30407006 0.30407006 0.71731631 0.71731631 0.71731631\n",
      "    0.23962045 0.23962045 0.23962045]\n",
      "   [0.66582067 0.66582067 0.66582067 0.51900469 0.51900469 0.51900469\n",
      "    0.60774608 0.60774608 0.60774608]\n",
      "   [0.66582067 0.66582067 0.66582067 0.51900469 0.51900469 0.51900469\n",
      "    0.60774608 0.60774608 0.60774608]\n",
      "   [0.66582067 0.66582067 0.66582067 0.51900469 0.51900469 0.51900469\n",
      "    0.60774608 0.60774608 0.60774608]]]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 构造原矩阵\n",
    "original_matrix = np.random.rand(1, 3, 3, 3)\n",
    "print(original_matrix)\n",
    "\n",
    "# 按行和列方向分别重复3次\n",
    "expanded_matrix = np.repeat(original_matrix, 3, axis=2)\n",
    "expanded_matrix = np.repeat(expanded_matrix, 3, axis=3)\n",
    "\n",
    "# 打印扩张后的矩阵\n",
    "print(expanded_matrix.shape)\n",
    "print(expanded_matrix)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 4, 4])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "a = torch.rand((3, 4, 4))\n",
    "print(a.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 2, 8])\n"
     ]
    }
   ],
   "source": [
    "b = [3, 2, 8]\n",
    "\n",
    "c = a.reshape(b)\n",
    "\n",
    "print(c.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[[0.8107, 0.3983, 0.1808,  ..., 0.2377, 0.1444, 0.6997],\n",
      "          [0.6381, 0.0871, 0.9162,  ..., 0.0882, 0.3530, 0.5687],\n",
      "          [0.4739, 0.5411, 0.6394,  ..., 0.1067, 0.8816, 0.9428],\n",
      "          ...,\n",
      "          [0.7280, 0.8044, 0.0895,  ..., 0.6929, 0.6038, 0.8415],\n",
      "          [0.0327, 0.4628, 0.0804,  ..., 0.1914, 0.1186, 0.2523],\n",
      "          [0.3599, 0.8601, 0.4092,  ..., 0.7141, 0.2099, 0.2207]],\n",
      "\n",
      "         [[0.1113, 0.7847, 0.0803,  ..., 0.2128, 0.5008, 0.7687],\n",
      "          [0.7530, 0.9965, 0.1397,  ..., 0.3661, 0.7860, 0.8186],\n",
      "          [0.9810, 0.0355, 0.9538,  ..., 0.6338, 0.1818, 0.3390],\n",
      "          ...,\n",
      "          [0.9932, 0.6923, 0.9655,  ..., 0.3242, 0.9802, 0.2531],\n",
      "          [0.8362, 0.5283, 0.3196,  ..., 0.4123, 0.1190, 0.8441],\n",
      "          [0.7796, 0.0463, 0.3480,  ..., 0.8512, 0.1110, 0.6207]],\n",
      "\n",
      "         [[0.9186, 0.6721, 0.2800,  ..., 0.7371, 0.5092, 0.3652],\n",
      "          [0.3589, 0.2630, 0.6807,  ..., 0.4572, 0.9795, 0.1170],\n",
      "          [0.9708, 0.3078, 0.0210,  ..., 0.4086, 0.2484, 0.8843],\n",
      "          ...,\n",
      "          [0.1224, 0.0964, 0.1806,  ..., 0.5518, 0.5906, 0.4027],\n",
      "          [0.0688, 0.0643, 0.1787,  ..., 0.7928, 0.3769, 0.1187],\n",
      "          [0.4861, 0.2321, 0.6475,  ..., 0.4754, 0.0229, 0.6025]]]])\n",
      "torch.Size([1, 3, 225, 225])\n",
      "torch.Size([1, 3, 224, 224])\n",
      "tensor([[[[0.8107, 0.8107, 0.8107,  ..., 0.1444, 0.6997, 0.6997],\n",
      "          [0.8107, 0.8107, 0.8107,  ..., 0.1444, 0.6997, 0.6997],\n",
      "          [0.8107, 0.8107, 0.8107,  ..., 0.1444, 0.6997, 0.6997],\n",
      "          ...,\n",
      "          [0.0327, 0.0327, 0.0327,  ..., 0.1186, 0.2523, 0.2523],\n",
      "          [0.3599, 0.3599, 0.3599,  ..., 0.2099, 0.2207, 0.2207],\n",
      "          [0.3599, 0.3599, 0.3599,  ..., 0.2099, 0.2207, 0.2207]],\n",
      "\n",
      "         [[0.1113, 0.1113, 0.1113,  ..., 0.5008, 0.7687, 0.7687],\n",
      "          [0.1113, 0.1113, 0.1113,  ..., 0.5008, 0.7687, 0.7687],\n",
      "          [0.1113, 0.1113, 0.1113,  ..., 0.5008, 0.7687, 0.7687],\n",
      "          ...,\n",
      "          [0.8362, 0.8362, 0.8362,  ..., 0.1190, 0.8441, 0.8441],\n",
      "          [0.7796, 0.7796, 0.7796,  ..., 0.1110, 0.6207, 0.6207],\n",
      "          [0.7796, 0.7796, 0.7796,  ..., 0.1110, 0.6207, 0.6207]],\n",
      "\n",
      "         [[0.9186, 0.9186, 0.9186,  ..., 0.5092, 0.3652, 0.3652],\n",
      "          [0.9186, 0.9186, 0.9186,  ..., 0.5092, 0.3652, 0.3652],\n",
      "          [0.9186, 0.9186, 0.9186,  ..., 0.5092, 0.3652, 0.3652],\n",
      "          ...,\n",
      "          [0.0688, 0.0688, 0.0688,  ..., 0.3769, 0.1187, 0.1187],\n",
      "          [0.4861, 0.4861, 0.4861,  ..., 0.0229, 0.6025, 0.6025],\n",
      "          [0.4861, 0.4861, 0.4861,  ..., 0.0229, 0.6025, 0.6025]]]])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "\n",
    "pertubations = torch.rand((1, 3, 75, 75))\n",
    "print(pertubations)\n",
    "\n",
    "expand_matrix = torch.repeat_interleave(pertubations, 3, dim=2)\n",
    "expand_matrix = torch.repeat_interleave(expand_matrix, 3, dim=3)\n",
    "print(expand_matrix.shape)\n",
    "expand_matrix = expand_matrix[:, :, :224, :224]\n",
    "print(expand_matrix.shape)\n",
    "print(expand_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 224, 224])\n",
      "torch.Size([1, 3, 224, 224])\n"
     ]
    }
   ],
   "source": [
    "block_size = 1\n",
    "print(expand_matrix.shape)\n",
    "new_matrix = expand_matrix[:, :, ::block_size, ::block_size]\n",
    "\n",
    "print(new_matrix.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[[0.9032, 0.1631, 0.5004,  ..., 0.4310, 0.9718, 0.1478],\n",
      "          [0.4859, 0.5180, 0.7671,  ..., 0.8536, 0.1432, 0.9194],\n",
      "          [0.2214, 0.7315, 0.8334,  ..., 0.1916, 0.4915, 0.5007],\n",
      "          ...,\n",
      "          [0.5542, 0.9932, 0.2328,  ..., 0.6588, 0.7011, 0.8799],\n",
      "          [0.2228, 0.0440, 0.6513,  ..., 0.7353, 0.8406, 0.4520],\n",
      "          [0.8690, 0.2597, 0.6669,  ..., 0.0519, 0.8404, 0.5027]],\n",
      "\n",
      "         [[0.7737, 0.8996, 0.9468,  ..., 0.1524, 0.2566, 0.8373],\n",
      "          [0.0525, 0.4091, 0.9430,  ..., 0.9758, 0.4352, 0.0603],\n",
      "          [0.0609, 0.6067, 0.6999,  ..., 0.2659, 0.1369, 0.6141],\n",
      "          ...,\n",
      "          [0.4348, 0.9032, 0.6334,  ..., 0.5737, 0.9937, 0.5339],\n",
      "          [0.4203, 0.5810, 0.6440,  ..., 0.0358, 0.1057, 0.9302],\n",
      "          [0.0548, 0.3064, 0.9664,  ..., 0.0943, 0.0714, 0.4466]],\n",
      "\n",
      "         [[0.4660, 0.2729, 0.3107,  ..., 0.2563, 0.8820, 0.4821],\n",
      "          [0.7253, 0.7168, 0.9034,  ..., 0.0572, 0.2638, 0.2303],\n",
      "          [0.1108, 0.2854, 0.7908,  ..., 0.4371, 0.3649, 0.8129],\n",
      "          ...,\n",
      "          [0.9614, 0.3565, 0.7981,  ..., 0.7067, 0.9042, 0.3973],\n",
      "          [0.7335, 0.7376, 0.1489,  ..., 0.1281, 0.0693, 0.1900],\n",
      "          [0.1176, 0.6035, 0.4314,  ..., 0.7157, 0.1131, 0.7154]]]])\n"
     ]
    }
   ],
   "source": [
    "print(new_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "2-3.pt\n",
      "2-3\n",
      "./data/BaseSample/CNN/FMNIST/Target/Batch(16)_Size(10)_Mode(independent)/Eps(0.1000)_Norm(inf)_Method(integer)/2-3.pt\n",
      "4-5.pt\n",
      "4-5\n",
      "./data/BaseSample/CNN/FMNIST/Target/Batch(16)_Size(10)_Mode(independent)/Eps(0.1000)_Norm(inf)_Method(integer)/4-5.pt\n",
      "1-2.pt\n",
      "1-2\n",
      "./data/BaseSample/CNN/FMNIST/Target/Batch(16)_Size(10)_Mode(independent)/Eps(0.1000)_Norm(inf)_Method(integer)/1-2.pt\n",
      "6-7.pt\n",
      "6-7\n",
      "./data/BaseSample/CNN/FMNIST/Target/Batch(16)_Size(10)_Mode(independent)/Eps(0.1000)_Norm(inf)_Method(integer)/6-7.pt\n",
      "7-5.pt\n",
      "7-5\n",
      "./data/BaseSample/CNN/FMNIST/Target/Batch(16)_Size(10)_Mode(independent)/Eps(0.1000)_Norm(inf)_Method(integer)/7-5.pt\n",
      "8-9.pt\n",
      "8-9\n",
      "./data/BaseSample/CNN/FMNIST/Target/Batch(16)_Size(10)_Mode(independent)/Eps(0.1000)_Norm(inf)_Method(integer)/8-9.pt\n",
      "0-1.pt\n",
      "0-1\n",
      "./data/BaseSample/CNN/FMNIST/Target/Batch(16)_Size(10)_Mode(independent)/Eps(0.1000)_Norm(inf)_Method(integer)/0-1.pt\n",
      "7-8.pt\n",
      "7-8\n",
      "./data/BaseSample/CNN/FMNIST/Target/Batch(16)_Size(10)_Mode(independent)/Eps(0.1000)_Norm(inf)_Method(integer)/7-8.pt\n",
      "5-6.pt\n",
      "5-6\n",
      "./data/BaseSample/CNN/FMNIST/Target/Batch(16)_Size(10)_Mode(independent)/Eps(0.1000)_Norm(inf)_Method(integer)/5-6.pt\n",
      "3-4.pt\n",
      "3-4\n",
      "./data/BaseSample/CNN/FMNIST/Target/Batch(16)_Size(10)_Mode(independent)/Eps(0.1000)_Norm(inf)_Method(integer)/3-4.pt\n",
      "9-0.pt\n",
      "9-0\n",
      "./data/BaseSample/CNN/FMNIST/Target/Batch(16)_Size(10)_Mode(independent)/Eps(0.1000)_Norm(inf)_Method(integer)/9-0.pt\n",
      "Number: 11\n",
      "2-3.pt\n",
      "4-5.pt\n",
      "1-2.pt\n",
      "6-7.pt\n",
      "7-5.pt\n",
      "8-9.pt\n",
      "0-1.pt\n",
      "7-8.pt\n",
      "5-6.pt\n",
      "3-4.pt\n",
      "9-0.pt\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "folder_path = \"./data/BaseSample/CNN/FMNIST/Target/Batch(16)_Size(10)_Mode(independent)/Eps(0.1000)_Norm(inf)_Method(integer)\"\n",
    "print(os.path.exists(folder_path))\n",
    "for root, dirs, files in os.walk(folder_path):\n",
    "    for filename in files:\n",
    "        print(filename)\n",
    "        print(filename[:-3])\n",
    "        print(os.path.join(root, filename))\n",
    "files = os.listdir(folder_path)\n",
    "num = len(files)\n",
    "\n",
    "print('Number: {}'.format(num))\n",
    "\n",
    "for file in files:\n",
    "\n",
    "    print(file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([10, 3, 32, 32])\n",
      "0\n",
      "torch.Size([3, 32, 32])\n",
      "\n",
      "1\n",
      "torch.Size([3, 32, 32])\n",
      "\n",
      "2\n",
      "torch.Size([3, 32, 32])\n",
      "\n",
      "3\n",
      "torch.Size([3, 32, 32])\n",
      "\n",
      "4\n",
      "torch.Size([3, 32, 32])\n",
      "\n",
      "5\n",
      "torch.Size([3, 32, 32])\n",
      "\n",
      "6\n",
      "torch.Size([3, 32, 32])\n",
      "\n",
      "7\n",
      "torch.Size([3, 32, 32])\n",
      "\n",
      "8\n",
      "torch.Size([3, 32, 32])\n",
      "\n",
      "9\n",
      "torch.Size([3, 32, 32])\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljj/anaconda3/envs/boundary/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "datas = torch.randn((10, 3, 32, 32))\n",
    "\n",
    "print(datas.shape)\n",
    "\n",
    "for index, data in enumerate(datas):\n",
    "\n",
    "    print(index)\n",
    "    print(data.shape)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.73834923 0.7152202  0.38585784 0.55027201 0.89369463]\n",
      " [0.38798616 0.22112618 0.81903115 0.43397028 0.93662914]\n",
      " [0.38595432 0.10394067 0.86251379 0.77713096 0.41095149]\n",
      " [0.01004234 0.15790298 0.59485632 0.56765533 0.58948582]\n",
      " [0.12658923 0.01891414 0.0612704  0.7473742  0.64328715]\n",
      " [0.78319437 0.12958176 0.46929609 0.00580074 0.98974869]\n",
      " [0.0579383  0.2688357  0.3166809  0.6835716  0.87143801]\n",
      " [0.20065926 0.08183809 0.29960943 0.4137806  0.99377035]\n",
      " [0.35233035 0.89738394 0.0530039  0.61288862 0.88623519]\n",
      " [0.71820815 0.33527677 0.73681654 0.20139813 0.71862394]\n",
      " [0.45682269 0.89811886 0.65969585 0.85863558 0.55374578]\n",
      " [0.64899987 0.81762954 0.24374549 0.41549333 0.34411504]\n",
      " [0.36921709 0.60249706 0.78507803 0.2882556  0.39706365]\n",
      " [0.67393046 0.73102385 0.89824524 0.18349626 0.99064952]\n",
      " [0.74754784 0.57632736 0.00857306 0.29080377 0.92251109]\n",
      " [0.93018428 0.32732434 0.2861567  0.69540987 0.70315912]\n",
      " [0.68135037 0.82069767 0.13018411 0.85056385 0.40323105]\n",
      " [0.42608393 0.14485254 0.09763327 0.76918499 0.15054324]\n",
      " [0.71474132 0.25240249 0.16211678 0.88090686 0.75285078]\n",
      " [0.2887567  0.01709399 0.6224093  0.69456976 0.10979374]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.random.rand(20, 5)\n",
    "\n",
    "print(arr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr[10:20] = np.random.rand(10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.73834923 0.7152202  0.38585784 0.55027201 0.89369463]\n",
      " [0.38798616 0.22112618 0.81903115 0.43397028 0.93662914]\n",
      " [0.38595432 0.10394067 0.86251379 0.77713096 0.41095149]\n",
      " [0.01004234 0.15790298 0.59485632 0.56765533 0.58948582]\n",
      " [0.12658923 0.01891414 0.0612704  0.7473742  0.64328715]\n",
      " [0.78319437 0.12958176 0.46929609 0.00580074 0.98974869]\n",
      " [0.0579383  0.2688357  0.3166809  0.6835716  0.87143801]\n",
      " [0.20065926 0.08183809 0.29960943 0.4137806  0.99377035]\n",
      " [0.35233035 0.89738394 0.0530039  0.61288862 0.88623519]\n",
      " [0.71820815 0.33527677 0.73681654 0.20139813 0.71862394]\n",
      " [0.43042066 0.47769374 0.37180409 0.55663756 0.99790874]\n",
      " [0.71884121 0.99211707 0.59835034 0.51081823 0.88352948]\n",
      " [0.3674342  0.16906392 0.44411718 0.42404029 0.07637961]\n",
      " [0.45948169 0.12906979 0.23410543 0.01086849 0.80420973]\n",
      " [0.99056201 0.71726246 0.10323025 0.90518436 0.36145768]\n",
      " [0.18174826 0.43022226 0.11405452 0.40677683 0.47091826]\n",
      " [0.31653301 0.04015357 0.53753904 0.43594776 0.87141149]\n",
      " [0.32376209 0.59454751 0.57767291 0.54249487 0.02797717]\n",
      " [0.58335351 0.99756004 0.42685263 0.37257458 0.49614264]\n",
      " [0.36446023 0.01353235 0.76927144 0.81796203 0.31583886]]\n"
     ]
    }
   ],
   "source": [
    "print(arr)"
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
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   "file_extension": ".py",
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